A car-carrying robot and its car-carrying method

Abstract

The present invention relates to a vehicle-carrying robot and a vehicle-carrying method thereof. The key points of the technical solution are as follows: The method includes: obtaining the relative pose between the vehicle-carrying robot and the vehicle to be carried; controlling the vehicle-carrying robot to align with the bottom of the vehicle to be carried according to the relative pose; controlling the vehicle-carrying robot to drill into the bottom of the vehicle to be carried and align with the wheels of the vehicle to be carried; controlling the four clamping arm assemblies of the vehicle-carrying robot to close correspondingly, lift the vehicle to be carried, and move it to the position to be carried. This application has the advantages of good real-time performance, and the vehicle-carrying process can be adjusted in real time according to the position of the vehicle-carrying robot and the vehicle to be carried.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle moving robots, and more particularly to a vehicle moving robot and a vehicle moving method thereof. Background Art

[0002] With the continuous increase in the number of household cars, most first- and second-tier cities have encountered the problem of difficult and slow parking. Therefore, more and more new parking lots with digitalization, intelligence and automation have been launched. Among them, the configuration based on multi-storey parking lots and car moving robots has obvious advantages, mainly because this solution can avoid complex mechanical structures and high maintenance costs in the later stage.

[0003] However, since drivers park their vehicles in different positions and car models in the garage, the car moving function of the automatic car moving robot becomes one of the pain points.

[0004] At present, the invention patent with patent application number CN202010950529.5 discloses a method for self-learning car moving of a parking robot. This parking method requires the identification of vehicle information, and then controls the car moving robot to move the car after identifying the model, size, parking position and posture of the vehicle. This car moving method has high requirements on the positioning accuracy of the car moving robot and is difficult to adjust in real time. In actual applications, it is easy for the car moving robot to fail to align with the vehicle after arriving at the designated position, and then it is difficult to move the vehicle. Therefore, there is still room for improvement. Summary of the invention

[0005] In view of the deficiencies in the prior art, the object of the present invention is to provide a vehicle moving robot and a vehicle moving method thereof, which have the advantages of good real-time performance and the vehicle moving process can be adjusted in real time according to the position of the vehicle moving robot and the vehicle to be moved.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions: a car moving robot, comprising: a robot body, four clamping arm assemblies, two laser radars, and a controller; the four clamping arm assemblies are all arranged on the robot body; two pairs of infrared rangefinders are installed in the middle of each clamping arm assembly; the two laser radars are respectively arranged at the two ends of the robot body; the controller is arranged on the robot body; the robot body, four clamping arm assemblies, two laser radars and eight pairs of infrared rangefinders are all electrically connected to the controller.

[0007] Optionally, the robot body includes: a front vehicle drive mechanism, a rear vehicle drive mechanism, and a telescopic mechanism; the front vehicle drive mechanism is disposed at one end of the telescopic mechanism; the rear vehicle drive mechanism is disposed at the other end of the telescopic mechanism; the controller is disposed on the front vehicle drive mechanism; the two lidars are respectively disposed on the front vehicle drive mechanism and the rear vehicle drive mechanism; the front vehicle drive mechanism, the rear vehicle drive mechanism, and the telescopic mechanism are all electrically connected to the controller.

[0008] A vehicle handling method of a vehicle handling robot includes:

[0009] Obtaining the relative pose between the vehicle handling robot and the vehicle to be handled;

[0010] Controlling the vehicle handling robot to align with the bottom of the vehicle to be handled according to the relative pose;

[0011] Controlling the vehicle handling robot to drill under the vehicle to be handled and align with the wheels of the vehicle to be handled;

[0012] Controlling the four clamping arm assemblies of the vehicle handling robot to close correspondingly, lifting the vehicle to be handled, and moving it to the position to be handled.

[0013] Optionally, the obtaining the relative pose between the vehicle handling robot and the vehicle to be handled includes:

[0014] Obtaining parking space data and lidar point cloud data including the wheels in the vehicle to be handled, where the parking space data is the parking space position data of the parking space where the vehicle to be handled is parked;

[0015] Filtering the lidar point cloud data according to the parking space data to obtain a first vehicle lidar point set, and obtaining a vehicle positioning frame according to the first vehicle lidar point set;

[0016] Obtaining a plurality of wheel samples according to the vehicle positioning frame, a preset wheelbase range, and preset segmentation data;

[0017] Converting the first vehicle lidar point set into a template coordinate system according to a preset wheel template set and each wheel sample to obtain a corresponding second vehicle lidar point set, where the wheel template set is constructed according to a preset wheelbase range and a preset interval;

[0018] Determining the corresponding second vehicle lidar point set according to each wheel sample and determining the corresponding wheel template from the wheel template set, comparing each second vehicle lidar point set with the corresponding wheel template to obtain a comparison result, and selecting the vehicle to be handled lidar point set from all the second vehicle lidar point sets according to all the comparison results;

[0019] Performing point cloud classification and error minimization fitting on the vehicle to be handled lidar point set according to the wheel template corresponding to the vehicle to be handled lidar point set to obtain the relative pose between the vehicle handling robot and the vehicle to be handled.

[0020] Optionally, the alignment of the transporter robot with the bottom of the vehicle to be transported according to the relative pose includes:

[0021] Controlling the rotation of the transporter robot according to the relative pose so that the orientation of the transporter robot is the same as that of the vehicle to be transported;

[0022] Controlling the translation of the transporter robot according to the relative pose so that the transporter robot moves to the central axis of the vehicle to be transported;

[0023] Controlling the rotation of the transporter robot according to the relative pose so that the orientation of the transporter robot is the same as that of the vehicle to be transported.

[0024] Optionally, the alignment of the transporter robot with the bottom of the vehicle to be transported according to the relative pose further includes:

[0025] Obtaining the angular deviation value between the transporter robot and the vehicle to be transported according to the relative pose to obtain the control parameter for rotation;

[0026] Obtaining the position and pose of the vehicle to be transported according to the relative pose to generate the central axis of the vehicle to be transported; calculating the distance from the transporter robot to the central axis of the vehicle to be transported to obtain the control parameter for translation.

[0027] Optionally, the control of the transporter robot to drill into the bottom of the vehicle to be transported and align with the wheels of the vehicle to be transported includes:

[0028] Obtaining all the motion control quantities of the transporter robot and generating Nj trajectory samples;

[0029] Calculating the cost of all trajectory samples;

[0030] Selecting the motion control quantity corresponding to the trajectory sample with the lowest cost to control the transporter robot to move to the bottom of the vehicle to be transported and align with the wheels of the vehicle to be transported.

[0031] Optionally, the calculation of the cost of all trajectory samples includes:

[0032] Obtaining the obstacle distance cost, the reference trajectory deviation cost, and the control quantity cost of all trajectory samples; the obstacle distance cost is the distance between the nearest obstacle on the trajectory corresponding to the trajectory sample and the trajectory of the transporter robot; the reference trajectory deviation cost is the distance between the trajectory corresponding to the trajectory sample and the reference trajectory; the control quantity cost is the rotation angle of the steering wheel of the transporter robot.

[0033] Optionally, the selection of the trajectory sample with the lowest cost includes:

[0034] Compare the obstacle long-term and short-term costs of Nj trajectory samples with a preset obstacle cost threshold respectively, and remove the trajectory samples with obstacle long-term and short-term costs less than the preset obstacle cost threshold to obtain Ni trajectory samples;

[0035] Calculate the weighted sum of the reference trajectory deviation cost and the control quantity cost of Ni trajectory samples respectively, and select the trajectory sample with the lowest weighted sum.

[0036] Optionally, controlling the transfer cart robot to move to the bottom of the vehicle to be transferred and align with the wheels of the vehicle to be transferred includes:

[0037] Control the transfer cart robot to move to the bottom of the vehicle to be transferred, and determine whether the rear vehicle drive mechanism of the transfer cart robot is aligned with the rear wheels of the vehicle to be transferred. If so, drive the front wheel drive mechanism to move through the telescopic mechanism and align the front wheel drive mechanism with the front wheels of the vehicle to be transferred; if not, return to the step of obtaining all the motion control quantities of the transfer cart robot and generating Nj trajectory samples.

[0038] In summary, the present invention has the following beneficial effects: Calculate the deviation pose between the transfer cart robot and the vehicle to be transferred through two lidar sensors of the transfer cart robot, and calculate the control quantities of translation and rotation through the deviation pose to control the transfer cart robot to perform translation and rotation, so that the transfer cart robot is aligned with the vehicle to be transferred. Subsequently, control the transfer cart robot to move in the direction farthest from the obstacle, which can ensure that the transfer cart robot can pass through the bottom of the vehicle. At the same time, adjust the transfer cart robot according to the alignment relationship between the rear vehicle drive mechanism and the wheels. After the rear vehicle drive mechanism is aligned with the wheels, drive the front vehicle drive mechanism to move linearly through the telescopic device, and after the front vehicle drive mechanism is aligned with the wheels, control all the clamping arm assemblies to close to clamp the wheels, thereby lifting the vehicle to be transferred. Then, drive the vehicle to be transferred through the front vehicle drive mechanism and the rear vehicle drive mechanism and place the vehicle to be transferred at the position to be transferred, and the vehicle handling can be completed; Since the transfer cart robot calculates the cost of several trajectory samples as the movement trajectory of the transfer cart robot, it eliminates the requirement for high-precision positioning of the transfer cart robot, and the obstacle distance measurement speed is relatively fast, so it can feedback the relative position between the transfer cart robot and the vehicle to be transferred in real time, and the transfer cart robot can be adjusted in real time, so as to quickly align with the wheels and carry the vehicle. Description of the Drawings

[0039] Figure 1 is the overall structural schematic diagram of the transfer cart robot of the present invention;

[0040] Figure 2 is the circuit module diagram of the transfer cart robot of the present invention;

[0041] Figure 3 is the flow schematic diagram of the transfer method of the present invention.

[0042] In the figure: 1. Robot body; 11. Front vehicle drive mechanism; 12. Rear vehicle drive mechanism; 13. Telescopic mechanism; 2. Clamping arm assembly; 3. Lidar; 4. Controller; 5. Infrared rangefinder. Specific embodiments

[0043] In order to make the objectives, features, and advantages of the present invention more obvious and understandable, the following provides a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings. Several embodiments of the present invention are shown in the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein.

[0044] In the present invention, unless otherwise clearly defined and limited, terms such as "installed", "connected", "connected to", "fixed", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features.

[0045] In the present invention, unless otherwise clearly defined and limited, the fact that the first feature is "above" or "below" the second feature may include the direct contact between the first and second features, or may include the situation where the first and second features are not in direct contact but in contact through other features between them. Moreover, the fact that the first feature is "above", "above the top", and "on the top" of the second feature includes that the first feature is directly above and obliquely above the second feature, or simply means that the horizontal height of the first feature is higher than that of the second feature. The fact that the first feature is "below", "below the bottom", and "under the bottom" of the second feature includes that the first feature is directly below and obliquely below the second feature, or simply means that the horizontal height of the first feature is lower than that of the second feature. The terms "vertical", "horizontal", "left", "right", "up", "down", and similar expressions are only for the purpose of illustration and do not indicate or imply that the indicated device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation on the present invention.

[0046] The following provides a detailed description of the present invention with reference to the accompanying drawings and embodiments.

[0047] The present invention provides a vehicle handling robot, as Figure 1 and Figure 2As shown in the figure, it includes: a robot body 1, four clamping arm assemblies 2, two lidar sensors 3, and a controller 4; the four clamping arm assemblies 2 are all arranged on the robot body 1; two pairs of infrared rangefinders 5 are installed in the middle of each clamping arm assembly 2; the two lidar sensors 3 are respectively arranged at both ends of the robot body 1; the controller 4 is arranged on the robot body 1; the robot body 1, the four clamping arm assemblies 2, the two lidar sensors 3, and eight pairs of infrared rangefinders 5 are all electrically connected to the controller 4.

[0048] In practical applications, the two lidar sensors 3 are respectively installed on the front and rear vehicles, and can obtain the road conditions in front of and behind the robot body 1. The controller 4 can control the vehicle positioning of the robot during obstacle avoidance, map positioning, and vehicle drilling according to the radar information transmitted by the lidar sensors 3; the clamping arm assembly 2 includes two symmetrically designed clamping arms, which can rotate around the rotating shaft and rotate in the same direction synchronously through gears during rotation. A number of bearings are installed on the clamping arms to hold the four wheels of the vehicle, and two pairs of infrared rangefinders 5 are installed in the middle of each clamping arm assembly 2 to judge whether the wheels are aligned with the clamping arm assembly 2 during the vehicle drilling process. In this embodiment, the controller 4 is an MCU.

[0049] Furthermore, the robot body 1 includes: a front vehicle driving mechanism 11, a rear vehicle driving mechanism 12, and a telescopic mechanism 13; the front vehicle driving mechanism 11 is arranged at one end of the telescopic mechanism 13; the rear vehicle driving mechanism 12 is arranged at the other end of the telescopic mechanism 13; the controller 4 is arranged on the front vehicle driving mechanism 11; the two lidar sensors 3 are respectively arranged on the front vehicle driving mechanism 11 and the rear vehicle driving mechanism 12; the front vehicle driving mechanism 11, the rear vehicle driving mechanism 12, and the telescopic mechanism 13 are all electrically connected to the controller 4. In practical applications, the telescopic mechanism 13 is used to control the front vehicle driving mechanism 11 and the rear vehicle driving mechanism 12 to move away from or close to each other, and both the front vehicle driving mechanism 11 and the rear vehicle driving mechanism 12 have walking wheels and steering wheels. By the cooperation of the steering wheels and the walking wheels, the robot body 1 can be controlled to translate and turn, so that the robot body 1 can be aligned with the vehicle to be moved.

[0050] Based on the above vehicle-carrying robot, the present invention also provides a vehicle-carrying method for the vehicle-carrying robot, as Figure 3 shown, including:

[0051] Step 100, obtaining the relative pose between the vehicle-carrying robot and the vehicle to be moved;

[0052] Step 200, controlling the vehicle-carrying robot to be aligned with the bottom of the vehicle to be moved according to the relative pose;

[0053] Step 300, controlling the vehicle-carrying robot to drill into the bottom of the vehicle to be moved and align with the wheels of the vehicle to be moved;

[0054] Step 400: Control the four clamping arm assemblies 2 of the car transporter robot to close correspondingly, lift the vehicle to be transported, and move it to the position to be transported.

[0055] In practical applications, first, the relative pose between the car transporter robot and the vehicle to be transported is obtained through the lidar 3. The relative pose includes the angle deviation value between the car transporter robot and the vehicle to be transported, the position and pose of the vehicle to be transported. Then, the car transporter robot is controlled to move according to the relative pose so that the bottom of the car transporter robot is aligned with the bottom of the vehicle to be transported. Subsequently, the car transporter robot is controlled to drill under the vehicle to be transported, and the infrared rangefinder 5 is used to determine whether the car transporter robot is aligned with the wheels of the vehicle to be transported. The clamping arm assemblies 2 on the front vehicle driving mechanism 11 and the rear vehicle driving mechanism 12 and the telescopic mechanism 13 are used to align the clamping arm assemblies 2 on the front vehicle driving mechanism 11 with the wheels of the vehicle to be transported, and at the same time, the clamping arm assemblies 2 on the rear vehicle driving mechanism 12 are aligned with the wheels of the vehicle to be transported. Then, control the four clamping arm assemblies 2 to close correspondingly, clamp the wheels to be transported, and drive the vehicle to be transported to move and turn through the front vehicle driving mechanism 11 and the rear vehicle driving mechanism 12 until the vehicle to be transported is moved to the position to be transported.

[0056] Optionally, obtaining the relative pose between the car transporter robot and the vehicle to be transported includes:

[0057] Obtain the parking space data and the laser point cloud data including the wheels in the vehicle to be transported. The parking space data is the parking space position data of the parking space where the vehicle to be transported is parked.

[0058] Filter the laser point cloud data according to the parking space data to obtain the first vehicle laser point set, and obtain the vehicle positioning frame according to the first vehicle laser point set.

[0059] Obtain a plurality of wheel samples according to the vehicle positioning frame, the preset wheelbase range, and the preset segmentation data.

[0060] Convert the first vehicle laser point set to the template coordinate system according to the preset wheel template set and each wheel sample to obtain the corresponding second vehicle laser point set. Among them, the wheel template set is constructed according to the preset wheelbase range and the preset interval.

[0061] Determine the corresponding second vehicle laser point set according to each wheel sample and determine the corresponding wheel template from the wheel template set. Compare each second vehicle laser point set with the corresponding wheel template to obtain a comparison result. Select the laser point set of the vehicle to be transported from all the second vehicle laser point sets according to all the comparison results.

[0062] Perform point cloud classification and minimum error fitting on the laser point set of the vehicle to be transported according to the wheel template corresponding to the laser point set of the vehicle to be transported to obtain the relative pose between the car transporter robot and the vehicle to be transported.

[0063] In practical applications, since the positions of all parking spaces have been determined when the parking lot is established, the specific position of a vehicle can be determined based on the vehicle's GPS information. Thus, according to the specific position of the vehicle, it can be determined which specific parking space the vehicle is parked in the parking lot. Obtaining the position data of this parking space is also obtaining the said parking space data. The laser point cloud data is obtained by the lidar 3 on the vehicle moving robot scanning the vehicle for one week. The lidar 3 can be a 16-line lidar 3. When parking, the vehicle is usually located within the parking space. Therefore, filtering the laser point cloud data with the parking space data can filter out the laser data points that do not belong to the vehicle, obtaining the laser data points representing the approximate position of the vehicle to form the first vehicle laser point set. Based on the first vehicle laser point set, a vehicle positioning frame is obtained. The vehicle positioning frame is segmented by a preset wheelbase range and preset segmentation data to form multiple wheel samples with different wheelbases. Then, according to the corresponding relationship between each wheel sample and the corresponding wheel template, the laser data points in the first laser point set are transformed to obtain the corresponding second vehicle laser point set. Then, each second vehicle laser point set is compared with the corresponding wheel template to obtain a comparison result. The second vehicle laser point set with the highest similarity in the comparison result is used as the target vehicle laser point set, that is, the wheelbase of the vehicle is determined. The wheelbase of this vehicle is denoted as the target wheelbase, and the wheel template corresponding to the target vehicle laser point set is denoted as the target wheel template. The target wheel template is the wheel template most suitable for the said vehicle. Based on the target wheel template, point cloud classification and error minimization fitting are performed on the target vehicle laser point set to obtain the left wheel position, right wheel position, and wheel attitude of the vehicle, that is, the target vehicle pose is obtained, which is convenient for the vehicle moving robot to perform vehicle moving processing.

[0064] Optionally, the controlling the vehicle moving robot to align with the bottom of the vehicle to be moved according to the relative pose includes:

[0065] Controlling the vehicle moving robot to rotate according to the relative pose so that the orientation of the vehicle moving robot is the same as that of the vehicle to be moved;

[0066] Controlling the vehicle moving robot to translate according to the relative pose so that the vehicle moving robot moves to the central axis of the vehicle to be moved;

[0067] Controlling the vehicle moving robot to rotate according to the relative pose so that the orientation of the vehicle moving robot is the same as that of the vehicle to be moved.

[0068] In practical applications, due to the differences in the poses between the car-lifting robot and the vehicle to be lifted, it is necessary to first rotate the car-lifting robot according to the relative pose so that the orientation of the car-lifting robot is the same as that of the vehicle to be lifted. Subsequently, the car-lifting robot is controlled to translate according to the relative position between the car-lifting robot and the vehicle to be lifted, so that the car-lifting robot moves to the central axis of the vehicle, which is convenient for the subsequent car-lifting robot to drill into the vehicle. Moreover, due to the different road surface conditions in the parking lot, the car-lifting robot may slip during translation, resulting in a difference in the pose between the car-lifting robot and the vehicle to be lifted after translation. Therefore, it is necessary to obtain the relative pose between the car-lifting robot and the vehicle to be lifted again after the translation of the car-lifting robot. And in the case where there is a difference in the pose between the car-lifting robot and the vehicle to be lifted after translation, a secondary rotation is performed to align the bottom of the car-lifting robot with the bottom of the vehicle to be lifted.

[0069] Further, the step of controlling the car-lifting robot to align with the bottom of the vehicle to be lifted according to the relative pose further includes:

[0070] Obtaining the angular deviation value between the car-lifting robot and the vehicle to be lifted according to the relative pose to obtain the control parameters for rotation;

[0071] Obtaining the position and pose of the vehicle to be lifted according to the relative pose to generate the central axis of the vehicle to be lifted; calculating the distance from the car-lifting robot to the central axis of the vehicle to be lifted to obtain the control parameters for translation.

[0072] In practical applications, by obtaining the angular deviation value between the car-lifting robot and the vehicle to be lifted according to the relative pose, the angle by which the car-lifting robot needs to rotate can be obtained, and this angle is used as the control parameter to control the front vehicle drive mechanism 11 and the rear vehicle drive mechanism 12 to turn. Subsequently, by obtaining the position and pose of the vehicle to be lifted according to the relative pose, the central axis of the vehicle to be lifted can be obtained, and according to the distance from the car-lifting robot to the central axis of the vehicle to be lifted, this distance is used as the control parameter for translation. When controlling the car-lifting robot to translate, the car-lifting robot is made to move towards the central axis by this distance, and thus the car-lifting robot can be moved to the central axis of the vehicle to be lifted.

[0073] Further, the step of controlling the car-lifting robot to drill into the bottom of the vehicle to be lifted and align with the wheels of the vehicle to be lifted includes:

[0074] Obtaining all the motion control quantities of the car-lifting robot and generating Nj trajectory samples;

[0075] Calculating the cost of all the trajectory samples;

[0076] Selecting the motion control quantity corresponding to the trajectory sample with the lowest cost to control the car-lifting robot to move to the bottom of the vehicle to be lifted and align with the wheels of the vehicle to be lifted.

[0077] In practical applications, the car carrier robot moves in a straight line in different directions of motion, and Nj different motion trajectories can be obtained. Combining all possible motion control quantities of the car carrier robot with the motion trajectories, Nj trajectory samples are generated. Subsequently, the cost of each trajectory is calculated respectively. The greater the cost, the greater the motion control quantity of the car carrier robot. Therefore, it is necessary to select the trajectory sample with the lowest cost to quickly move the car carrier robot to the bottom of the vehicle to be carried, and make the front vehicle drive mechanism 11 and the rear vehicle drive mechanism 12 align with the corresponding wheels respectively.

[0078] Further, the calculation of the cost of all trajectory samples includes:

[0079] Obtain the obstacle distance cost, reference trajectory deviation cost, and control quantity cost of all trajectory samples; the obstacle distance cost is the distance between the nearest obstacle on the trajectory corresponding to the trajectory sample and the trajectory of the car carrier robot; the reference trajectory deviation cost is the distance between the trajectory corresponding to the trajectory sample and the reference trajectory; the control quantity cost is the rotation angle of the steering wheel of the car carrier robot.

[0080] In practical applications, the obstacle distance cost (OC): that is, the distance between the nearest obstacle on a specific trajectory and the trajectory of the current car carrier robot. This trajectory distance refers to the distance that the robot needs to travel along this trajectory until it collides with an obstacle; the reference trajectory deviation cost (TC): Since the vehicle body is positioned before drilling the vehicle, a reference trajectory can be generated according to this positioning. The distance between the selected trajectory and the reference trajectory is the reference trajectory deviation cost; the control quantity cost (CC): A specific trajectory requires the robot to rotate the steering wheel. The larger the required rotation angle, the greater the control quantity cost.

[0081] Further, the selection of the trajectory sample with the lowest cost includes:

[0082] Compare the obstacle distance costs of the Nj trajectory samples with a preset obstacle cost threshold respectively, and eliminate the trajectory samples with obstacle distance costs less than the preset obstacle cost threshold to obtain Ni trajectory samples;

[0083] Calculate the weighted sum of the reference trajectory deviation cost and the control quantity cost of the Ni trajectory samples respectively, and select the trajectory sample with the lowest weighted sum.

[0084] In practical applications, once the three costs are obtained, the following rules can be used to select the trajectory with the least cost: Design an obstacle threshold Ot in advance. Trajectories less than this threshold can be discarded. Therefore, Ni seam trajectories can be screened out from the Nj trajectory samples by this threshold. That is, by walking along the seam trajectory, it is impossible for the car carrier robot to collide within a distance of Ot. On the basis of the Ni seam trajectories, the weighted sum of the TC and CC costs is calculated to obtain the trajectory with the optimal cost.

[0085] If among the j trajectories, for a certain trajectory, Ot > Oc, then this trajectory is excluded. For the remaining i trajectories, Costi = Wit * TC + Wic * CC is calculated respectively. Wit and Wic are the reference trajectory deviation cost and control quantity cost weights respectively, and the specific values of the weights can be obtained through experiments. After calculating Costi, the Costs of each trajectory are compared, and the trajectory with the lowest Cost is selected.

[0086] Further, controlling the car lifter robot to move to the bottom of the vehicle to be lifted and align with the wheels of the vehicle to be lifted includes:

[0087] Controlling the car lifter robot to move to the bottom of the vehicle to be lifted, determining whether the rear vehicle driving mechanism 12 of the car lifter robot is aligned with the rear wheels of the vehicle to be lifted. If so, driving the front vehicle driving mechanism to move through the telescopic mechanism 13 and aligning the front vehicle driving mechanism with the front wheels of the vehicle to be lifted; if not, returning to the step of obtaining all the motion control quantities of the car lifter robot and generating Nj trajectory samples.

[0088] In practical applications, the car lifter robot is controlled to move to the bottom of the vehicle through the above trajectories, and four groups of infrared rangefinders 5 on the rear vehicle driving mechanism 12 are used to measure the tires on both sides of the rear vehicle driving mechanism 12 respectively. When the distances measured by the two infrared rangefinders 5 on both sides are within a specific range and the deviation of the measured distances on both sides is less than the preset value, it is determined that the two clamping arm assemblies 2 on the rear vehicle driving mechanism 12 are aligned with the rear wheels of the vehicle to be lifted; after the rear vehicle driving mechanism 12 is aligned, the front vehicle driving mechanism 11 is pushed to move through the telescopic mechanism 13, so that the front vehicle driving mechanism 11 moves in a straight line, and four groups of infrared rangefinders 5 on the front vehicle driving mechanism 11 are used to measure the tires on both sides of the front vehicle driving mechanism 11 respectively. When the distances measured by the two infrared rangefinders 5 on both sides are within a specific range and the deviation of the measured distances on both sides is less than the preset value, it is determined that the two clamping arm assemblies 2 on the front vehicle driving mechanism 11 are aligned with the front wheels of the vehicle to be lifted.

[0089] In the specific implementation process, the deviation pose between the car-lifting robot and the vehicle to be lifted is calculated by the two lidar sensors 3 of the car-lifting robot, and the control quantities for translation and rotation are calculated based on the deviation pose to control the car-lifting robot to perform translation and rotation, so that the car-lifting robot is aligned with the vehicle to be lifted. Subsequently, the car-lifting robot is controlled to move in the direction farthest from the obstacle, which can ensure that the car-lifting robot can pass through the bottom of the vehicle. At the same time, the alignment relationship between the rear vehicle drive mechanism 12 and the wheels is used to adjust the car-lifting robot. After the rear vehicle drive mechanism 12 is aligned with the wheels, the front vehicle drive mechanism 11 is driven to move linearly by the telescopic device. After the front vehicle drive mechanism 11 is aligned with the wheels, all the clamping arm assemblies 2 are controlled to close to clamp the wheels, thereby lifting the vehicle to be lifted. Then, the vehicle to be lifted is driven by the front vehicle drive mechanism 11 and the rear vehicle drive mechanism 12 and placed at the position to be lifted, and the vehicle handling can be completed. Since the car-lifting robot calculates the cost of several trajectory samples as the movement trajectory of the car-lifting robot, the requirement for high-precision positioning of the car-lifting robot is eliminated, and the obstacle distance measurement speed is relatively fast. Therefore, the relative position between the car-lifting robot and the vehicle to be lifted can be feedback in real time, and the car-lifting robot can be adjusted in real time, so as to quickly align with the wheels and lift the vehicle.

[0090] The above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A vehicle handling method, characterized in that, it includes: Obtaining the relative pose between the vehicle handling robot and the vehicle to be handled; Controlling the vehicle handling robot to align with the bottom of the vehicle to be handled according to the relative pose; Controlling the vehicle handling robot to drill into the bottom of the vehicle to be handled and align with the wheels of the vehicle to be handled; Controlling the four clamping arm assemblies of the vehicle handling robot to close correspondingly, lift the vehicle to be handled, and move it to the position to be handled; The obtaining of the relative pose between the vehicle handling robot and the vehicle to be handled includes: Obtaining the parking space data and the laser point cloud data containing the wheels in the vehicle to be handled, where the parking space data is the parking space position data of the parking space where the vehicle to be handled is parked; Filtering the laser point cloud data according to the parking space data to obtain the first vehicle laser point set, and obtaining the vehicle positioning frame according to the first vehicle laser point set; Obtaining a plurality of wheel samples according to the vehicle positioning frame, the preset wheelbase range, and the preset segmentation data; Converting the first vehicle laser point set into the template coordinate system according to the preset wheel template set and each wheel sample to obtain the corresponding second vehicle laser point set, where the wheel template set is constructed according to the preset wheelbase range and the preset interval; Determining the corresponding second vehicle laser point set according to each wheel sample and determining the corresponding wheel template from the wheel template set, comparing each second vehicle laser point set with the corresponding wheel template to obtain a comparison result, and selecting the laser point set of the vehicle to be handled from all the second vehicle laser point sets according to all the comparison results; Performing point cloud classification and error minimization fitting on the laser point set of the vehicle to be handled according to the wheel template corresponding to the laser point set of the vehicle to be handled to obtain the relative pose between the vehicle handling robot and the vehicle to be handled; The controlling the vehicle handling robot to drill into the bottom of the vehicle to be handled and align with the wheels of the vehicle to be handled includes: Obtaining all the motion control quantities of the vehicle handling robot and generating Nj trajectory samples; Calculating the cost of all the trajectory samples; Selecting the motion control quantity corresponding to the trajectory sample with the lowest cost to control the vehicle handling robot to move to the bottom of the vehicle to be handled and align with the wheels of the vehicle to be handled.

2. The method according to claim 1, characterized in that, The controlling the vehicle handling robot to align with the bottom of the vehicle to be handled according to the relative pose includes: Controlling the vehicle handling robot to rotate according to the relative pose so that the orientation of the vehicle handling robot is consistent with the orientation of the vehicle to be handled; Controlling the vehicle handling robot to translate according to the relative pose so that the vehicle handling robot moves to the central axis of the vehicle to be handled; Controlling the vehicle handling robot to rotate according to the relative pose so that the orientation of the vehicle handling robot is consistent with the orientation of the vehicle to be handled.

3. The method according to claim 2, characterized in that, The controlling the vehicle handling robot to align with the bottom of the vehicle to be handled according to the relative pose further includes: Obtaining the angle deviation value between the vehicle handling robot and the vehicle to be handled according to the relative pose to obtain the control parameter for rotation; Obtaining the position and attitude of the vehicle to be handled according to the relative pose, generating the central axis of the vehicle to be handled; calculating the distance from the vehicle handling robot to the central axis of the vehicle to be handled to obtain the control parameter for translation.

4. The method according to claim 1, It is characterized in that The cost calculation of all trajectory samples includes: Obtaining the obstacle distance cost, reference trajectory deviation cost, and control amount cost of all trajectory samples; the obstacle distance cost is the trajectory distance between the nearest obstacle on the trajectory corresponding to the trajectory sample and the trajectory of the car-carrying robot; the reference trajectory deviation cost is the distance between the trajectory corresponding to the trajectory sample and the reference trajectory; the control amount cost is the rotation angle of the steering wheel of the car-carrying robot.

5. The method according to claim 1, It is characterized in that The selection of the trajectory sample with the lowest cost includes: Comparing the obstacle distance costs of Nj trajectory samples with a preset obstacle cost threshold respectively, and removing the trajectory samples with obstacle distance costs less than the preset obstacle cost threshold to obtain Ni trajectory samples; Calculating the weighted sum of the reference trajectory deviation cost and the control amount cost of Ni trajectory samples respectively, and selecting the trajectory sample with the lowest weighted sum.

6. The method according to claim 1, It is characterized in that The control of the car-carrying robot to move to the bottom of the vehicle to be carried and align with the wheels of the vehicle to be carried includes: Controlling the car-carrying robot to move to the bottom of the vehicle to be carried, and judging whether the rear vehicle driving mechanism of the car-carrying robot is aligned with the rear wheels of the vehicle to be carried. If so, driving the front vehicle driving mechanism to move through the telescopic mechanism and aligning the front vehicle driving mechanism with the front wheels of the vehicle to be carried; if not, returning to the step of obtaining all the motion control amounts of the car-carrying robot and generating Nj trajectory samples.

7. A car-carrying robot based on the car-carrying method according to claim 1, It is characterized in that Including: A robot body, four clamping arm assemblies, two lidars, and a controller; the four clamping arm assemblies are all arranged on the robot body; two pairs of infrared rangefinders are installed in the middle of each clamping arm assembly; the two lidars are respectively arranged at both ends of the robot body; the controller is arranged on the robot body; the robot body, the four clamping arm assemblies, the two lidars, and eight pairs of infrared rangefinders are all electrically connected to the controller; The robot body includes: a front vehicle driving mechanism, a rear vehicle driving mechanism, and a telescopic mechanism; the front vehicle driving mechanism is arranged at one end of the telescopic mechanism; the rear vehicle driving mechanism is arranged at the other end of the telescopic mechanism; the controller is arranged on the front vehicle driving mechanism; the two lidars are respectively arranged on the front vehicle driving mechanism and the rear vehicle driving mechanism; the front vehicle driving mechanism, the rear vehicle driving mechanism, and the telescopic mechanism are all electrically connected to the controller.

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